Strategies related to histogram analysis and filtering similar to Galvan et al. Their technique works effectively in specific conditions on double compressed images exploiting the a-priori knowledge of monotonicity of the DCT coefficients by histogram iterative refinement. Other techniques based on statistical consideration of Discrete Cosine Transform (DCT) histograms were proposed by Galvan et al. They proposed a method based on the Expectation Maximization algorithm to predict the most probable quantization factors of the primary compression over a set of candidates. different datasets in the double compressed scenario.Ī first technique for FQE was proposed by Bianchi et al. First Quantization Estimation (FQE) has been widely investigated for both the aligned and non-aligned cases w.r.t. Once an image has been detected to be multiply compressed, the reconstruction of the history of the image itself becomes challenging. In particular, pushes the detection up to triple compression by defining a three-class classification problem demonstrated to work only for multiple compressed images with the same Quality Factor. The robust inference of how many times an image has been compressed is a problem investigated with techniques working mainly for the aligned scenario (). This becomes particularly relevant when dealing with multiple compressions. State-of-the-art image forensics techniques usually make use of different underlying assumptions specifically addressed for the task (). The problem is furtherly complicated by considering the possibility to crop and/or resize images (e.g., aligned and non-aligned scenario ). Even detecting if an investigated image has been compressed only two times is a challenging task, namely Double Compression Detection (). This makes forensics analysis really difficult in order to reconstruct the history of an image from the first acquisition device to each of the subsequent processing (). In each step, the image could go through a modification that potentially changes something without modifying (in almost cases) the semantic content. The life-cycle of a digital image is extremely complicated nowadays: images are acquired by smartphones or digital cameras, edited, shared through Instant Messaging platforms, etc.
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